import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

'''
    本例子进行网络训练过程的可视化
'''

def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):

    layer_name = 'layer%s'%n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size,out_size],name="W"))
            tf.summary.histogram(layer_name+"/weights",Weights)
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1,out_size])+0.1,name="b")
            tf.summary.histogram(layer_name+"/biases",biases)

        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs,Weights),biases)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        tf.summary.histogram(layer_name+"/outputs",outputs)
        return outputs


x_data = np.linspace(-1, 1, 300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

with tf.name_scope('inputs'): # tensorboard中框架方框
    xs = tf.placeholder(tf.float32,[None,1],name="X_INPUT")
    ys = tf.placeholder(tf.float32,[None,1],name="Y_INPUT")

# 输入层
l1 = add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu)
# 输出层
prediction = add_layer(l1,10,1,n_layer=2,activation_function=None)

with tf.name_scope('Loss'): # tensorboard中框架方框
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction-ys),reduction_indices=[1]),name="Loss")
    tf.summary.scalar('loss',loss) # 记录标量使用scalar
with tf.name_scope('train_step'): # tensorboard中框架方框
    train_step = tf.train.AdamOptimizer(0.1).minimize(loss)

init = tf.initialize_all_variables()

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion()  # 避免在show时暂停显示
plt.show()
with tf.Session() as sess:
    sess.run(init)
    # writer = tf.train.SummaryWriter("logs/",sess.graph) #版本不一样，使用的函数也不一样
    merged = tf.summary.merge_all() # 将上面的所有值进行merge
    writer = tf.summary.FileWriter("logs/",sess.graph) # 记录网络图
    for step  in range(1000):
        sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
        if step % 20 == 0:
            # print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
            try:
                # 为了连续显示，去除上一步的线
                ax.lines.remove(lines[0])
            except Exception:
                pass
            prediction_value=sess.run(prediction,feed_dict={xs:x_data,ys:y_data})
            lines = ax.plot(x_data,prediction_value,'r-',lw=5)
            plt.pause(0.1)
            result = sess.run(merged,feed_dict={xs:x_data,ys:y_data})
            writer.add_summary(result,step) # 将merge的结果进行可视化